Bibliographic record
Abstract
Obesity has more than doubled worldwide since 1980 and it has become the focus of public health due to a wide range of serious complications. It is believed to be a complex disorder triggered by multiple genes, environmental factors and their interactions. The total number of single nucleotide polymorphisms (SNPs) associated with adult body mass index (BMI) at genome-wide significance level (P<5×10-8) has recently increased to 136. However, these genome-wide association studies (GWAS) have been conducted primarily in populations of European ancestry. This thesis aims to: 1) investigate whether these BMI SNPs are also associated with BMI in other ethnicities (South Asian, East Asian, African, Latino American and Native American) using a multi-ethnic prospective EpiDREAM cohort study; 2) explore the parental and child genetic contributions to obesity-related traits in children from birth to 5 years in the FAMILY cohort; 3) examine the maternal and child genetic contribution of BMI SNPs to maternal gestational weight gain (GWG) and postpartum weight retention in the FAMILY cohort. The major findings are: 1) most BMI susceptibility genes identified in Europeans are also associated with BMI in other five ethnicities. The effects of some SNPs and BMI genetic risk score (GRS) were modified by ethnicity; 2) SNPs contributing to adult BMI start to exert their effect at birth and in early childhood. Parent-of-origin effects may occur in a limited subset of obesity predisposing SNPs; and 3) there is no association between maternal and child GRS and GWG. But there is a genetic link between pre-pregnancy BMI variation and offspring birth weight and maternal postpartum weight retention. Taken together, these findings indicate that GWAS of specific ethnic group, children, birth weight and GWG are necessary to look for novel variants and alternative pathways influencing the development of obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".